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Medical Image Classification
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Medical Image Classification is a task in medical image analysis that involves classifying medical images, such as X-rays, MRI scans, and CT scans, into different categories based on the type of image or the presence of specific structures or diseases. The goal is to use computer algorithms to automatically identify and classify medical images based on their content, which can help in diagnosis, treatment planning, and disease monitoring.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Robust Training with Data Augmentation for Medical Imaging Classification
Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach
Detecção da Psoríase Utilizando Visão Computacional: Uma Abordagem Comparativa Entre CNNs e Vision Transformers
Recent Advances in Medical Image Classification
Deep Modeling and Optimization of Medical Image Classification
Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models
EVM-Fusion: An Explainable Vision Mamba Architecture with Neural Algorithmic Fusion
Feature Preserving Shrinkage on Bayesian Neural Networks via the R2D2 Prior
Learning Concept-Driven Logical Rules for Interpretable and Generalizable Medical Image Classification
Expert-Like Reparameterization of Heterogeneous Pyramid Receptive Fields in Efficient CNNs for Fair Medical Image Classification
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output Codes
Adapting a Segmentation Foundation Model for Medical Image Classification
V-EfficientNets: Vector-Valued Efficiently Scaled Convolutional Neural Network Models
CLOG-CD: Curriculum Learning based on Oscillating Granularity of Class Decomposed Medical Image Classification
A Deep Bayesian Convolutional Spiking Neural Network-based CAD system with Uncertainty Quantification for Medical Images Classification
CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning
Embedding Radiomics into Vision Transformers for Multimodal Medical Image Classification
A Hybrid Fully Convolutional CNN-Transformer Model for Inherently Interpretable Medical Image Classification
Federated Learning for Medical Image Classification: A Comprehensive Benchmark
Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability
Diffusion models applied to skin and oral cancer classification
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matching
Keeping Representation Similarity in Finetuning for Medical Image Analysis
XFMamba: Cross-Fusion Mamba for Multi-View Medical Image Classification
AI-Augmented Thyroid Scintigraphy for Robust Classification
MedKAN: An Advanced Kolmogorov-Arnold Network for Medical Image Classification
Can Score-Based Generative Modeling Effectively Handle Medical Image Classification?
Medical Image Classification with KAN-Integrated Transformers and Dilated Neighborhood Attention
Benchmarking MedMNIST dataset on real quantum hardware
Hierarchical Vision Transformer with Prototypes for Interpretable Medical Image Classification
Long-tailed Medical Diagnosis with Relation-aware Representation Learning and Iterative Classifier Calibration
Hybrid Deep Learning Framework for Classification of Kidney CT Images: Diagnosis of Stones, Cysts, and Tumors
CoRPA: Adversarial Image Generation for Chest X-rays Using Concept Vector Perturbations and Generative Models
ViT-2SPN: Vision Transformer-based Dual-Stream Self-Supervised Pretraining Networks for Retinal OCT Classification
Generating customized prompts for Zero-Shot Rare Event Medical Image Classification using LLM
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